Project

Welcome to my project page.

Keep an eye on this space to stay updated with my project activities.

1. Project Overview and Scope 🔎

Istanbul is not only Turkey’s but also Europe’s most populous city. Therefore, if a disaster such as an earthquake occurs, the potential of destruction and the need for help would be very high. Due to the earthquake disaster that occurred at the beginning of 2023, the risky condition of buildings and residences in Turkey has become a matter of concern for people.

The purpose of this project is to examine Istanbul, which is almost certain to face a possible earthquake and significant destruction. The aim of the project is not only to assess buildings and residences in terms of their risky structural condition and make district-based interpretations but also to evaluate the opinions of people living in Istanbul and compare all the results.

7 datasets were used for this purpose, various graphs were drawn and interpreted comprehensively to make inferences.

2. Data 📄

2.1 Data Source

• Deprem Senaryosu Analiz Sonuçları

• 2017 Yılı Mahalle Bazlı Bina Sayıları

• VDYM İlçe Bazında Hanelerdeki Riskli Yapı Durumu Bireysel Öngörüleri

• VDYM İlçe Bazında Hanelerdeki Kentsel Dönüşüm Fikri Bireysel Öngörüleri

• İlçe Bazlı Ortalama Hane Halkı Büyüklüğü

• 2019 Yılı Belediye Nüfusları

• İstanbul Çevresinde Gerçekleşen Depremler

Information and explanations about these datasets are included in the section 2.2 General Information About Data.

All this data was taken from İstanbul Büyükşehir Belediyesi Açık Veri Portalı and TÜİK Merkezi Dağıtım Sistemi.

Code

#install.packages("readxl")
library(readxl)

dosya_yolu<-"/Users/aycacetin/Documents/GitHub/emu660-spring2024-aycacetin/assets/deprem.xlsx"

deprem_senaryo<-read_excel(dosya_yolu, sheet=1)
bina_sayi<-read_excel(dosya_yolu, sheet=2)
riskli_yapi_durumu<-read_excel(dosya_yolu, sheet=3)
kentsel_donusum_fikri<-read_excel(dosya_yolu, sheet=4)
ort_hane<-read_excel(dosya_yolu, sheet=7)
belediye_nufus<-read_excel(dosya_yolu, sheet=8)
gerceklesen_deprem<-read_excel(dosya_yolu, sheet=9)

save(deprem_senaryo, file="deprem_senaryo.RData")
save(bina_sayi, file="bina_sayi.RData")
save(riskli_yapi_durumu, file="riskli_yapi_durumu.RData")
save(kentsel_donusum_fikri, file="kentsel_donusum_fikri.RData")
save(ort_hane, file="ort_hane.RData")
save(belediye_nufus, file="belediye_nufus.RData")
save(gerceklesen_deprem, file="gerceklesen_deprem.RData")

str(deprem_senaryo)

tibble [959 × 16] (S3: tbl_df/tbl/data.frame)
 $ id                          : num [1:959] 1 2 3 4 5 6 7 8 9 10 ...
 $ ilce_adi                    : chr [1:959] "ADALAR" "ADALAR" "ADALAR" "ADALAR" ...
 $ mahalle_adi                 : chr [1:959] "BURGAZADA" "HEYBELİADA" "KINALIADA" "MADEN" ...
 $ mahalle_koy_uavt            : num [1:959] 40139 40142 40143 40140 40141 ...
 $ cok_agir_hasarli_bina_sayisi: num [1:959] 54 101 53 104 101 1 2 1 1 1 ...
 $ agir_hasarli_bina_sayisi    : num [1:959] 99 175 97 192 180 3 13 6 6 5 ...
 $ orta_hasarli_bina_sayisi    : num [1:959] 256 423 287 483 445 21 108 51 49 23 ...
 $ hafif_hasarli_bina_sayisi   : num [1:959] 241 393 302 484 422 57 371 199 129 65 ...
 $ can_kaybi_sayisi            : num [1:959] 8 25 5 22 16 0 0 0 0 0 ...
 $ agir_yarali_sayisi          : num [1:959] 6 21 3 18 13 0 0 0 0 0 ...
 $ hastanede_tedavi_sayisi     : num [1:959] 24 66 15 64 48 1 8 2 2 0 ...
 $ hafif_yarali_sayisi         : num [1:959] 42 113 27 113 83 3 26 10 8 0 ...
 $ dogalgaz_boru_hasari        : num [1:959] 0 1 0 1 1 0 1 0 0 0 ...
 $ icme_suyu_boru_hasari       : num [1:959] 0 1 1 1 2 0 1 1 0 0 ...
 $ atik_su_boru_hasari         : num [1:959] 1 2 1 2 2 0 1 1 0 0 ...
 $ gecici_barinma              : num [1:959] 398 763 420 847 687 89 659 273 209 26 ...

Code

str(bina_sayi)

tibble [959 × 10] (S3: tbl_df/tbl/data.frame)
 $ id             : num [1:959] 1 2 3 4 5 6 7 8 9 10 ...
 $ ilce_adi       : chr [1:959] "ADALAR" "ADALAR" "ADALAR" "ADALAR" ...
 $ mahalle_adi    : chr [1:959] "BURGAZADA" "HEYBELİADA" "KINALIADA" "MADEN" ...
 $ mahalle_uavt   : num [1:959] 40139 40142 40143 40140 40141 ...
 $ 1980_oncesi    : num [1:959] 433 836 610 863 842 0 0 0 0 0 ...
 $ 1980-2000_arasi: num [1:959] 214 347 244 510 426 244 1360 685 565 332 ...
 $ 2000_sonrasi   : num [1:959] 173 212 158 308 217 121 845 589 216 184 ...
 $ 1-4 kat_arasi  : num [1:959] 802 1359 923 1637 1434 ...
 $ 5-9 kat_arasi  : num [1:959] 18 36 89 44 51 12 555 470 27 1 ...
 $ 9-19 kat_arasi : num [1:959] 0 0 0 0 0 0 3 7 0 0 ...

Code

str(riskli_yapi_durumu)

tibble [39 × 4] (S3: tbl_df/tbl/data.frame)
 $ ilce                  : chr [1:39] "ADALAR" "ARNAVUTKÖY" "ATAŞEHİR" "AVCILAR" ...
 $ riskli_yapi_evet      : num [1:39] 28 96 384 111 508 470 227 103 170 118 ...
 $ riskli_yapi_hayir     : num [1:39] 295 603 910 971 1176 ...
 $ riskli_yapi_bilmiyorum: num [1:39] 36 84 102 307 350 284 114 57 129 140 ...

Code

str(kentsel_donusum_fikri)

tibble [39 × 3] (S3: tbl_df/tbl/data.frame)
 $ ilce                         : chr [1:39] "ADALAR" "ARNAVUTKÖY" "ATAŞEHİR" "AVCILAR" ...
 $ kentsel_donusum_ihtiyac_evet : num [1:39] 40 100 457 240 607 536 263 59 190 158 ...
 $ kentsel_donusum_ihtiyac_hayir: num [1:39] 319 683 939 1148 1427 ...

Code

str(ort_hane)

tibble [39 × 2] (S3: tbl_df/tbl/data.frame)
 $ ilce                        : chr [1:39] "Adalar" "Arnavutköy" "Ataşehir" "Avcılar" ...
 $ ortalama_hanehalki_buyuklugu: num [1:39] 2.37 3.89 2.99 3.29 3.77 3.32 2.8 3.65 3.28 2.36 ...

Code

str(belediye_nufus)

tibble [39 × 2] (S3: tbl_df/tbl/data.frame)
 $ belediye  : chr [1:39] "Adalar Belediyesi" "Arnavutköy Belediyesi" "Ataşehir Belediyesi" "Avcılar Belediyesi" ...
 $ nufus_2019: num [1:39] 15238 282488 425094 448882 745125 ...

Code

str(gerceklesen_deprem)

tibble [349 × 8] (S3: tbl_df/tbl/data.frame)
 $ event_id           : chr [1:349] "20200204_0000090" "20200204_0000082" "20200204_0000067" "20200204_0000046" ...
 $ time               : chr [1:349] "2020-02-04T10:17:09.4Z" "2020-02-04T08:43:16.0Z" "2020-02-04T06:13:29.2Z" "2020-02-04T03:42:21.5Z" ...
 $ latitude           : num [1:349] 39 39 38.4 38.4 39.1 ...
 $ longitude          : num [1:349] 27.9 27.9 25.5 25.5 27.8 ...
 $ depth/km           : num [1:349] 6 10 15 17 9 12 10 2 7 9 ...
 $ mag_type           : chr [1:349] "ml" "ml" "ml" "ml" ...
 $ magnitude          : num [1:349] 3.4 4.1 3.3 3.3 3.3 3.4 3.4 3.3 4 3.5 ...
 $ event_location_name: chr [1:349] "WESTERN TURKEY" "WESTERN TURKEY" "AEGEAN SEA" "AEGEAN SEA" ...

2.2 General Information About Data

Deprem Senaryosu Analiz Sonuçları is a dataset containing the results of an analysis made on an earthquake scenario that is predicted to have a magnitude of 7.5. Each row represents a neighbourhood. The columns have information mainly about the number of buildings based on damage level, the number of people based on vital status and pipe damages.

2017 Yılı Mahalle Bazlı Bina Sayıları is a dataset about the buildings. Each row represents a neighbourhood. The columns have information mainly about year of construction and number of floors of the buildings.

VDYM İlçe Bazında Hanelerdeki Riskli Yapı Durumu Bireysel Öngörüleri is a dataset about the opinions of households about the risky building situation on a district basis.

VDYM İlçe Bazında Hanelerdeki Kentsel Dönüşüm Fikri Bireysel Öngörüleri is a dataset about the opinions of households about the idea of urban transformation on a district basis.

İlçe Bazlı Ortalama Hane Halkı Büyüklüğü is a dataset about the average household size on a district basis.

2019 Yılı Belediye Nüfusları is a dataset about the population within the borders of each municipality.

İstanbul Çevresinde Gerçekleşen Depremler is a dataset about the earthquakes around Istanbul within 1 year. Each row represents an earthquake. In the columns, there are details about the earthquakes.

2.3 Reason of Choice

Istanbul is a city where the risk of earthquake is said to be very high, and it is predicted to cause significant loss of life and property. Almost every day, warnings from experts about the earthquake, which is estimated to be between 7.2 and 7.6 in magnitude, can be seen in news and newspapers. I decided to work on such a current topic, to inform people through comprehensive analysis, to make them more aware, and to encourage taking steps against this disaster to get through with minimal damage.

2.4 Preprocessing

There are 7 datasets in “.csv”, “.xls” and “.xlsx” formats which were downloaded from the sources mentioned in the section 2.1 Data Source. All these datasets have been merged into the same Excel file called “deprem.xlsx” and organized for a better use in the analysis. Each dataset is placed on a different sheet. After that, spelling errors on each dataset were corrected via Excel. In other words, correction of wrong letters was done. Finally, the “deprem.xlsx” file was read and stored in .RData format using the code in the section 2.1 Data Source. The .RData file can be downloaded to review.

3. Analysis 🖥️

3.1 Exploratory Data Analysis

Analysis of the Datasets

Code

#install.packages("ggplot2")
library(ggplot2)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Code

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1
✔ readr     2.1.5     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

“deprem_senaryo” analysis:

The analysis began with the “deprem_senaryo” dataset. Initially, plots were created using data about the number of damaged buildings, the number of injured people and the number of pipeline damages. And then, the need for temporary shelter was assesed.

Although the data is based on neighborhoods, the analyses have been done based on districts.

“ilçe bazında hasarlı bina sayıları” (4 types of damage):

• Bakırköy, Fatih and Küçükçekmece on the “Çok Ağır Hasarlı Binalar” plot,

• Avıclar and Bakırköy on the “Ağır Hasarlı Binalar” plot,

• Avcılar on the “Orta Hasarlı Binalar” and “Hafif Hasarlı Binalar” plot were attention-grabbing.

Code

p1<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=cok_agir_hasarli_bina_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Çok Ağır Hasarlı Bina Sayısı")+
  labs(title="İlçe Bazında Çok Ağır Hasarlı Bina Sayısı Dağılımı")
p1

Code

p2<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=agir_hasarli_bina_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Ağır Hasarlı Bina Sayısı")+
  labs(title="İlçe Bazında Ağır Hasarlı Bina Sayısı Dağılımı")
p2

Code

p3<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=orta_hasarli_bina_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Orta Hasarlı Bina Sayısı")+
  labs(title="İlçe Bazında Orta Hasarlı Bina Sayısı Dağılımı")
p3

Code

p4<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=hafif_hasarli_bina_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Hafif Hasarlı Bina Sayısı")+
  labs(title="İlçe Bazında Hafif Hasarlı Bina Sayısı Dağılımı")
p4

“kişi sayıları” (4 types):

• Bahçelievler and Bakırköy on the “Can Kaybı” plot,

• Bahçelievler, Bakırköy, Fatih and Küçükçekmece on the “Ağır Yaralı” plot,

• Bahçelievler, Bakırköy and Küçükçekmece on the “Hastanede Tedavi” and “Hafif Yaralı” plot were attention-grabbing.

Code

p5<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=can_kaybi_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Can Kaybı Sayısı")+
  labs(title="İlçe Bazında Can Kaybı Sayısı Dağılımı")
p5

Code

p6<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=agir_yarali_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Ağır Yaralı Sayısı")+
  labs(title="İlçe Bazında Ağır Yaralı Sayısı Dağılımı")
p6

Code

p7<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=hastanede_tedavi_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Hastanede Tedavi Sayısı")+
  labs(title="İlçe Bazında Hastanede Tedavi Sayısı Dağılımı")
p7

Code

p8<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=hafif_yarali_sayisi))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Hafif Yaralı Sayısı")+
  labs(title="İlçe Bazında Hafif Yaralı Sayısı Dağılımı")
p8

“boru hasarı” (3 types of damage):

• Avcılar and Beylikdüzü on the “Doğalgaz Boru Hasarı” plot,

• Beylikdüzü on the “İçme Suyu Boru Hasarı” plot,

• Avcılar, Bakırköy, Beylikdüzü and Tuzla on the “Atık Su Boru Hasarı” plot were attention-grabbing.

Code

p9<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=dogalgaz_boru_hasari))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Doğalgaz Boru Hasarı")+
  labs(title="İlçe Bazında Doğalgaz Boru Hasarı Dağılımı")
p9

Code

p10<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=icme_suyu_boru_hasari))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("İçme Suyu Boru Hasarı")+
  labs(title="İlçe Bazında İçme Suyu Boru Hasarı Dağılımı")
p10

Code

p11<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=atik_su_boru_hasari))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Atık Su Boru Hasarı")+
  labs(title="İlçe Bazında Atık Su Boru Hasarı Dağılımı")
p11

“geçici barınma”:

• Avcılar, Bahçelievler, Beylikdüzü, Küçükçekmece and Zeytinburnu on the “Atık Su Boru Hasarı” plot were attention-grabbing.

Code

p12<-deprem_senaryo%>%ggplot(aes(x=ilce_adi, y=gecici_barinma))+
  geom_point()+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Geçici Barınma")+
  labs(title="İlçe Bazında Geçici Barınma Dağılımı")
p12

“bina_sayi” analysis:

The analysis continued with the “bina_sayi” dataset, evaluating buildings based on their construction year and number of floors.

Although the data is based on neighborhoods, the analyses have been done based on districts.

“ilçe bazında yıllara göre bina sayılarının oranları”:

It has been observed that the majority of the buildings were constructed between 1980 and 2000.

Code

once<-sum(bina_sayi$`1980_oncesi`)
ara<-sum(bina_sayi$`1980-2000_arasi`)
sonra<-sum(bina_sayi$`2000_sonrasi`)
oranlar1<-c(once=once/(once+ara+sonra), 
           ara=ara/(once+ara+sonra), 
           sonra=sonra/(once+ara+sonra))
print(oranlar1)

     once       ara     sonra 
0.2215122 0.4700309 0.3084569 

“ilçe bazında yıllara göre bina sayıları”:

• Beyoğlu, Fatih and Kadıköy on the “Yıllara Göre Bina Sayıları” plot were attention-grabbing.

Code

p13<-ggplot(bina_sayi, aes(x=ilce_adi))+
  geom_col(aes(y=`1980_oncesi`, fill="1980 Öncesi"), position=position_dodge(width=0.9))+
  geom_col(aes(y=`1980-2000_arasi`, fill="1980-2000 Arası"), position=position_dodge(width=0.9))+
  geom_col(aes(y=`2000_sonrasi`, fill="2000 Sonrası"), position=position_dodge(width=0.9))+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe")+
  ylab("Bina Sayısı")+
  labs(title="İlçe Bazında Yıllara Göre Bina Sayıları")
p13

“ilçe bazında kat sayısına göre bina sayılarının oranları”:

It has been observed that the majority of the buildings are 1-4 stories tall.

Code

alcak<-sum(bina_sayi$`1-4 kat_arasi`)
orta<-sum(bina_sayi$`5-9 kat_arasi`)
yuksek<-sum(bina_sayi$`9-19 kat_arasi`)
oranlar2<-c(alcak=alcak/(alcak+orta+yuksek), 
           orta=orta/(alcak+orta+yuksek), 
           yuksek=yuksek/(alcak+orta+yuksek))
print(oranlar2)

     alcak       orta     yuksek 
0.65751122 0.31906640 0.02342238 

“ilçe bazında kat sayısına göre bina sayıları”:

Code

p14<-ggplot(bina_sayi, aes(x=ilce_adi))+
  geom_col(aes(y=`1-4 kat_arasi`, fill="1-4 kat_arasi"), position=position_dodge(width=0.9))+
  geom_col(aes(y=`5-9 kat_arasi`, fill="5-9 kat_arasi"), position=position_dodge(width=0.9))+
  geom_col(aes(y=`9-19 kat_arasi`, fill="9-19 kat_arasi"), position=position_dodge(width=0.9))+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe")+
  ylab("Bina Sayısı")+
  labs(title="İlçe Bazında Kat Sayısına Göre Bina Sayıları")
p14

“riskli_yapi_durumu” analysis:

An analysis of individual perceptions regarding the structural risk of their households have been conducted.

“İlçe bazında hanelerdeki riskli yapı durumu hakkındaki bireysel öngörülerin oranları” (3 types of individual foresights):

It has been observed that a significant number of people answered “yes” or “I don’t know.”.

Code

evet<-sum(riskli_yapi_durumu$riskli_yapi_evet)
hayir<-sum(riskli_yapi_durumu$riskli_yapi_hayir)
bilmiyorum<-sum(riskli_yapi_durumu$riskli_yapi_bilmiyorum)
oranlar3<-c(riskli_yapi_evet=evet/(evet+hayir+bilmiyorum), 
           riskli_yapi_hayir=hayir/(evet+hayir+bilmiyorum), 
           riskli_yapi_bilmiyorum=bilmiyorum/(evet+hayir+bilmiyorum))
print(oranlar3)

      riskli_yapi_evet      riskli_yapi_hayir riskli_yapi_bilmiyorum 
             0.1793526              0.6675207              0.1531267 

“İlçe bazında hanelerdeki riskli yapı durumu hakkındaki bireysel öngörülerin dağılımı” (3 types of individual foresights):

In some districts, the green segments representing those who believe they live in risky structures are particularly noticeable.

Code

#veri çerçevesini uzun formata dönüştürme
library(tidyr)
riskli_yapi_durumu_duzenlenmis<-pivot_longer(riskli_yapi_durumu, cols=starts_with("riskli_yapi_"), names_to="ongoru", values_to="deger")

p15<-ggplot(riskli_yapi_durumu_duzenlenmis, aes(x=ilce, y=deger, fill=ongoru))+
  geom_bar(stat="identity", position="stack")+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe") +
  ylab("Öngörü Sayısı") +
  labs(title="İlçe Bazında Hanelerdeki Riskli Yapı Durumu Hakkındaki Bireysel Öngörüler")
p15

“kentsel_donusum_fikri” analysis:

An analysis of individual perceptions regarding the idea of urban transformation of their households have been conducted.

“İlçe bazında hanelerdeki kentsel dönüşüm fikri hakkındaki bireysel öngörülerin oranları” (2 types of individual foresights):

It has been observed that there is a significant number of people who consider urban transformation necessary and cannot be underestimated.

Code

evet<-sum(kentsel_donusum_fikri$kentsel_donusum_ihtiyac_evet)
hayir<-sum(kentsel_donusum_fikri$kentsel_donusum_ihtiyac_hayir)
oranlar4<-c(kentsel_donusum_ihtiyac_evet=evet/(evet+hayir), 
           kentsel_donusum_ihtiyac_hayir=hayir/(evet+hayir))
print(oranlar4)

 kentsel_donusum_ihtiyac_evet kentsel_donusum_ihtiyac_hayir 
                    0.2255722                     0.7744278 

“İlçe bazında hanelerdeki kentsel dönüşüm fikri hakkındaki bireysel öngörülerin dağılımı” (2 types of individual foresights):

In some districts, the red segments representing those who believe in the need of urban transformation are particularly noticeable.

Code

#veri çerçevesini uzun formata dönüştürme
library(tidyr)
kentsel_donusum_fikri_duzenlenmis<-pivot_longer(kentsel_donusum_fikri, cols=starts_with("kentsel_donusum_ihtiyac_"), names_to="ongoru", values_to="deger")

p16<-ggplot(kentsel_donusum_fikri_duzenlenmis, aes(x=ilce, y=deger, fill=ongoru))+
  geom_bar(stat="identity", position="stack")+
  theme(axis.text.x=element_text(angle=90, hjust=1))+
  xlab("İlçe") +
  ylab("Öngörü Sayısı") +
  labs(title="İlçe Bazında Hanelerdeki Kentsel Dönüşüm Fikri Hakkındaki Bireysel Öngörüler")
p16

“ort_hane” analysis:

“İlçe Bazlı Ortalama Hanehalkı Büyüklüğü”:

In the analysis of the “ort_hane” dataset, districts have been ranked from highest to lowest average household size and it has been observed that the most crowded households are in Sultanbeyli.

Code

#ortalama hanehalkı büyüklüğüne göre büyükten küçüğe sıralama
ort_hane2<-ort_hane%>%mutate(ilce=fct_reorder(ilce, ortalama_hanehalki_buyuklugu, .desc=TRUE))

p17<-ggplot(ort_hane2, aes(x=ilce, y=ortalama_hanehalki_buyuklugu))+
  geom_bar(stat="identity")+
  theme(axis.text.x=element_text(angle=45, hjust=1))+
  xlab("İlçe Adı")+
  ylab("Ortalama Hanehalkı Büyüklüğü")+
  labs(title="İlçe Bazında Ortalama Hanehalkı Büyüklüğü")
p17

We can see from the graph that in more than half of the districts in İstanbul, the average household size is more than 3.

“belediye_nufus” analysis:

“2019 Yılı Belediye Nüfusları” Grafiği:

In the analysis of the “belediye_nufus” dataset, municipalities have been ranked from highest to lowest population and it has been observed that the most crowded municipality is the Esenyurt Municipality.

Code

#ortalama hanehalkı büyüklüğüne göre büyükten küçüğe sıralama
belediye_nufus2<-belediye_nufus%>%mutate(belediye=fct_reorder(belediye, nufus_2019, .desc=TRUE))

p18<-ggplot(belediye_nufus2, aes(x=belediye, y=nufus_2019))+
  geom_bar(stat="identity")+
  theme(axis.text.x=element_text(angle=45, hjust=1))+
  xlab("Belediye Adı")+
  ylab("Ortalama Hanehalkı Büyüklüğü")+
  labs(title="Belediye Bazında 2019 Yılı Belediye Nüfusları Büyüklüğü")
p18

We can visualize the dataset titled “2019 Yılı Belediye Nüfusları”. We can see from the graph that 2 municipalities, Esenyurt and Küçükçekmece, are over 750.000. We can also see from the graph that the first one-third of the municipalities are over 500.000. From this, we can identify the most populous municipalities that would need the most help in an earthquake disaster.

“gerceklesen_deprem” analysis:

Analyzing this data could be crucial for examining earthquake activity and identifying potential risks. However, predicting future earthquake magnitudes is a complex thing to do because earthquakes are random and unpredictible.

“İstanbul Çevresinde Gerçekleşen Depremlerin Büyüklüklerinin Zaman Serisi Grafiği”:

Code

#time sütununu işleme
gerceklesen_deprem$time<-as.POSIXct(gerceklesen_deprem$time, format="%Y-%m-%dT%H:%M:%OSZ")

library(ggplot2)

p19<-ggplot(gerceklesen_deprem, aes(x=time, y=magnitude))+
  geom_line()+
  labs(title="İstanbul Çevresinde Gerçekleşen Depremlerin Büyüklüklükleri", x="Tarih", y="Büyüklük")+
  theme_minimal()+
  scale_x_datetime(date_breaks="1 month", date_labels="%b %Y")+
  theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1))
p19

Code

p20<-ggplot(gerceklesen_deprem, aes(x=time, y=`depth/km`))+
  geom_line()+
  labs(title="İstanbul Çevresinde Gerçekleşen Depremlerin Derinlikleri", x="Tarih", y="Derinlik")+
  theme_minimal()+
  scale_x_datetime(date_breaks="1 month", date_labels="%b %Y")+
  theme(axis.text.x=element_text(angle=90, vjust=0.5, hjust=1))
p20

What stands out in both graphs is the increase in earthquake frequencies after January 2020. This indicates that the danger is coming.

“İstanbul Çevresinde Gerçekleşen Depremlerin Harita Üzerinde Gösterimi”:

The created map allows us to observe whre earthquakes are concentrated.

Code

#leaflet paketini yükleme
#install.packages("leaflet")
library(leaflet)

#veri çerçevesi oluşturma
depremler<-data.frame(name=gerceklesen_deprem$event_id,
                   latitude=gerceklesen_deprem$latitude,
                   longitude=gerceklesen_deprem$longitude)

#İstanbul haritasını oluşturma
istanbul_map<-leaflet()%>%setView(lng=28.9784, lat=41.0082, zoom=11)%>%addTiles() 

#verileri haritaya ekleme
for(i in 1:nrow(depremler)){
  istanbul_map<-addMarkers(istanbul_map, lng=depremler$longitude[i], lat=depremler$latitude[i], popup=depremler$name[i])
  }

#haritayı görüntüleme
istanbul_map

District Based Overall Score Calculation, Sorting and Risk Analysis

A dataframe named “analiz” was constructed in order to contain the data to be used for calculating the overall risk score on a district basis. The rows represent the districts and the columns represent the factors. Each factor has a weight in order to calculate the overall risk score and the weights are located in the “weight” vector.

Code

#mahalle bazında veri içeren verisetlerinde ilçe bazında olacak şekilde düzeltmeler yapma
deprem_senaryo2<-deprem_senaryo%>%group_by(ilce_adi)%>%summarise(across(starts_with("cok_agir_hasarli_bina_sayisi"):starts_with("gecici_barinma"), sum))
bina_sayi2<-bina_sayi%>%group_by(ilce_adi)%>%summarise(across(starts_with("1980_oncesi"):starts_with("9-19 kat_arasi"), sum))

#"analiz" adlı bir dataframe oluşturma
analiz<-data.frame(ilce_adi=deprem_senaryo2$ilce_adi,
                   cok_agir_hasarli_bina_sayisi=deprem_senaryo2$cok_agir_hasarli_bina_sayisi,
                   agir_hasarli_bina_sayisi=deprem_senaryo2$agir_hasarli_bina_sayisi,
                   orta_hasarli_bina_sayisi=deprem_senaryo2$orta_hasarli_bina_sayisi,
                   hafif_hasarli_bina_sayisi=deprem_senaryo2$hafif_hasarli_bina_sayisi,
                   can_kaybi_sayisi=deprem_senaryo2$can_kaybi_sayisi,
                   agir_yarali_sayisi=deprem_senaryo2$agir_yarali_sayisi,
                   hastanede_tedavi_sayisi=deprem_senaryo2$hastanede_tedavi_sayisi,
                   hafif_yarali_sayisi=deprem_senaryo2$hafif_yarali_sayisi,
                   dogalgaz_boru_hasari=deprem_senaryo2$dogalgaz_boru_hasari,
                   icme_suyu_boru_hasari=deprem_senaryo2$icme_suyu_boru_hasari,
                   atik_su_boru_hasari=deprem_senaryo2$atik_su_boru_hasari,
                   gecici_barinma=deprem_senaryo2$gecici_barinma,
                   `1980_oncesi`=bina_sayi2$`1980_oncesi`,
                   `1980-2000_arasi`=bina_sayi2$`1980-2000_arasi`,
                   `2000_sonrasi`=bina_sayi2$`2000_sonrasi`,
                   `1-4 kat_arasi`=bina_sayi2$`1-4 kat_arasi`,
                   `5-9 kat_arasi`=bina_sayi2$`5-9 kat_arasi`,
                   `9-19 kat_arasi`=bina_sayi2$`9-19 kat_arasi`,
                   ortalama_hanehalki_buyuklugu=ort_hane$ortalama_hanehalki_buyuklugu)

#"weight" adlı bir vektör oluşturma
weight<-c(0.09,0.07,0.05,0.03,0.09,0.07,0.05,0.03,0.05,0.05,0.05,0.05,0.08,0.05,0.03,0.03,0.05,0.08)

#overall score hesaplama
overall_risk_score<-analiz$cok_agir_hasarli_bina_sayisi*weight[1]+
  analiz$agir_hasarli_bina_sayisi*weight[2]+
  analiz$orta_hasarli_bina_sayisi*weight[3]+
  analiz$hafif_hasarli_bina_sayisi*weight[4]+
  analiz$can_kaybi_sayisi*weight[5]+
  analiz$agir_yarali_sayisi*weight[6]+
  analiz$hastanede_tedavi_sayisi*weight[7]+
  analiz$hafif_yarali_sayisi*weight[8]+
  analiz$dogalgaz_boru_hasari*weight[9]+
  analiz$icme_suyu_boru_hasari*weight[10]+
  analiz$atik_su_boru_hasari*weight[11]+
  analiz$gecici_barinma*weight[12]+
  analiz$X1980_oncesi*analiz$ortalama_hanehalki_buyuklugu*weight[13]+
  analiz$X1980.2000_arasi*analiz$ortalama_hanehalki_buyuklugu*weight[14]+
  analiz$X2000_sonrasi*analiz$ortalama_hanehalki_buyuklugu*weight[15]+
  analiz$X1.4.kat_arasi*analiz$ortalama_hanehalki_buyuklugu*weight[16]+
  analiz$X5.9.kat_arasi*analiz$ortalama_hanehalki_buyuklugu*weight[17]+
  analiz$X9.19.kat_arasi*analiz$ortalama_hanehalki_buyuklugu*weight[18]

latitude=c(40.8747,41.1864,40.9833,40.9792,41.0341,40.9977,40.9804,41.0837,41.0349,41.0441,41.1271,41.0133,41.0371,41.0248,41.1421,41.0323,41.0542,41.0412,41.0551,41.0203,41.0576,41.0105,40.9903,41.0717,40.8999,41.0092,40.9339,40.8796,41.0090,41.1664,41.1749,41.0737,41.0604,40.9684,41.1070,40.8144,41.0338,41.0327,40.9910)
longitude=c(29.1294,28.7389,29.1278,28.7214,28.8330,28.8506,28.8724,28.8169,28.9122,29.0017,29.0978,28.6489,28.9774,28.5854,28.4575,29.1695,28.8676,28.6939,28.9346,28.9339,28.9153,28.8741,29.0205,28.9646,29.1936,28.7757,29.1650,29.2580,29.2109,29.0500,29.6096,28.2479,28.9878,29.2620,28.8714,29.3094,29.1013,29.0319,28.8968)

overall_risk_score_df<-data.frame(ilce_adi=analiz$ilce_adi, latitude=latitude, longitude=longitude, overall_risk_score=overall_risk_score)

After the overall score calculation was completed, ranking was performed and the districts were ranked from highest risk to lowest risk. Finally, the overall scores were shown on the map.

Code

overall_risk_score_df<-overall_risk_score_df[order(overall_risk_score_df$overall_risk_score, decreasing=TRUE), ]
overall_risk_score_df

        ilce_adi latitude longitude overall_risk_score
18         FATİH  41.0412   28.6939          20835.981
8       BAĞCILAR  41.0837   28.8169          18841.300
26        PENDİK  41.0092   28.7757          16853.602
24  KÜÇÜKÇEKMECE  41.0717   28.9646          16487.425
16      ESENYURT  41.0323   29.1695          15474.934
36      ÜMRANİYE  40.8144   29.3094          15326.482
37       ÜSKÜDAR  41.0338   29.1013          14236.909
28       SARIYER  40.8796   29.2580          14050.153
5   BAHÇELİEVLER  41.0341   28.8330          13016.060
17          EYÜP  41.0542   28.8676          12666.458
31       SİLİVRİ  41.1749   29.6096          10846.386
10        BEYKOZ  41.0441   29.0017          10844.370
22        KARTAL  41.0105   28.8741          10801.665
29   SULTANBEYLİ  41.0090   29.2109          10595.132
12       BEYOĞLU  41.0133   28.6489          10499.915
21       KADIKÖY  41.0576   28.9153          10492.666
19 GAZİOSMANPAŞA  41.0551   28.9346          10119.521
4        AVCILAR  40.9792   28.7214          10001.234
2     ARNAVUTKÖY  41.1864   28.7389           9778.344
25       MALTEPE  40.8999   29.1936           9714.603
30    SULTANGAZİ  41.1664   29.0500           9625.334
14  BÜYÜKÇEKMECE  41.0248   28.5854           9418.842
15       ESENLER  41.1421   28.4575           8572.687
3       ATAŞEHİR  40.9833   29.1278           8534.802
39         ŞİŞLİ  40.9910   28.8968           8420.029
32         TUZLA  41.0737   28.2479           8395.400
34       ÇATALCA  40.9684   29.2620           8070.917
27    SANCAKTEPE  40.9339   29.1650           7792.004
7     BAYRAMPAŞA  40.9804   28.8724           7772.034
9     BAŞAKŞEHİR  41.0349   28.9122           7732.653
23     KAĞITHANE  40.9903   29.0205           7608.464
35      ÇEKMEKÖY  41.1070   28.8714           6403.390
6       BAKIRKÖY  40.9977   28.8506           6223.436
33   ZEYTİNBURNU  41.0604   28.9878           6084.945
11    BEYLİKDÜZÜ  41.1271   29.0978           5217.615
20      GÜNGÖREN  41.0203   28.9339           5144.587
13      BEŞİKTAŞ  41.0371   28.9774           4984.912
38          ŞİLE  41.0327   29.0319           4655.740
1         ADALAR  40.8747   29.1294           1856.583

Code

#en riskli ilçeyi belirleme
highest_overall_risk_score<-overall_risk_score_df[which.max(overall_risk_score_df$overall_risk_score), "ilce_adi"]

risk_map<-leaflet(overall_risk_score_df)%>%addTiles()%>%addCircleMarkers(lng=~overall_risk_score_df$longitude, lat=~overall_risk_score_df$latitude, label=~paste0(overall_risk_score_df$ilce_adi, "<br>", "Overall Skor: ", overall_risk_score_df$overall_risk_score), color=ifelse(overall_risk_score_df$ilce_adi==highest_overall_risk_score, "red", "blue"), radius=5, stroke=FALSE, fillOpacity=0.8)%>%addLegend(position="bottomright", colors=c("red", "blue"), labels=c("En Riskli İlçe", "Diğer İlçeler"), title="Risk Seviyeleri")
risk_map

3.2 Results

As a result of these analyses, districts where precautions need to be taken, where the most loss of life and property could occur, and where potential interventions need to be well planned have been identified. These are Fatih, Bağcılar, Pendik, Küçükçekmece and Esenyurt. If the weights used in calculating the overall risk score are determined by experts, much more reliable and accurate results will be obtained.

4. Results and Key Takeaways 📈

Istanbul is Europe’s most populous city. It means that the potential of destruction and the need for help would be very high if a disaster such as an earthquake occurs.

The purpose of this project is to examine the risky structural condition, to make district-based interpretations and to evaluate the opinions of people living in Istanbul.

7 datasets were used in this study, plots were drawn and interpreted comprehensively.

• This study has determined which districts are most likely to experience possible building and infrastructure damage in an earthquake disaster. This helps determine where, how and how much aid should be directed.
• By calculating the overall risk scores, the districts of Istanbul have been ranked from the most risky to least risky. Thanks to his ranking, the government will be able to take appropriate steps both before and after an earthquake, aiding in preparedness and response efforts.
• Lastly, this study serves as both a guide and an encouragement for the society to be prepared for this disaster.

This study has

• identified the regions most prone to earthquakes and the cities most at risk,

• depicted them on maps,

• drawn conclusions about the opinions of survey participants and

• examined building and infrastructure in Istanbul.

*ChatGPT was utilized in this project.